Fast compressive Raman micro-spectroscopy to image and classify microplastics from natural marine environment

Clément Grand (1) , Camille Scotté (2, 1) , Énora Prado (3) , Maria El Rakwe (3) , Olivier Fauvarque (3) , Hervé Rigneault (1, 4)

1 FRESNEL - Institut FRESNEL
2 UMR ITAP - Technologies et Méthodes pour les Agricultures de demain
3 RDT - Unité Recherches et Développements Technologiques
4 MOSAIC - MOSAIC

The fast and reliable detection of micron-sized plastic particles from the natural marine environment is an important topic that is mostly addressed using spontaneous Raman spectroscopy. Due to the long (>tens of ms) integration time required to record a viable Raman signal, measurements are limited to a single point per microplastic particle or require very long acquisition times (up to tens of hours). In this work, we develop, validate, and demonstrate a compressive Raman technology using binary spectral filters and single-pixel detection that can image and classify six types of marine microplastic particles over an area of 1 mm2 with a pixel dwell time down to 1.75 ms/pixel and a spatial resolution of 1 µm. This is x10–100 faster than reported in previous studies.

Domaines: Optique [physics.optics]

Clément Grand, Camille Scotté, Énora Prado, Maria El Rakwe, Olivier Fauvarque, et al.. Fast compressive Raman micro-spectroscopy to image and classify microplastics from natural marine environment. Environmental Technology and Innovation, 2024, 34, pp.103622. ⟨10.1016/j.eti.2024.103622⟩. ⟨hal-04535494⟩